The present invention relates generally to predicting the behavior of a complex physical system with a qualitative analysis. More particularly, this invention pertains to improving the resolution of the prediction by correctly handling algebraic loops in physical system models for diagnosis applications. The prior art has recognized the ability to analyze and track the dynamic behavior of complex physical systems with abstracted simplified models in a qualitative reasoning framework. Typically the abstraction process creates lumped parameter models, which makes it hard to draw cause effect relation between individual component parameter changes and observed transients in the measurements. Our approach provides a more informative analysis by recognizing the presence of negative feedback effects in loop structures.
The Modeling and Analysis of Complex Systems group at Vanderbilt University School of Engineering, conducts research in the area of hybrid modeling and analysis of physical systems as well as monitoring, prediction and fault isolation (diagnosis) for dynamic continuous systems. A primary focus of the current research in this area is in developing hybrid models for analyzing mixed continuous and discrete behavior of engineering systems. Mixed behaviors are inherent in embedded systems, i.e., continuous processes controlled by discrete elements, such as PLCs (programmable logic arrays) and computers. Also, discontinuities may emerge in models of continuous physical systems as an artifact of abstracting physical phenomena that operate on a temporal or spatial scale much smaller than that of interest to the modeller. In this case, physical laws such as continuity of power and conservation of energy appear to be violated when discontinuities occur. Much of the research has been geared towards finding physical laws that govern the resulting discrete behavior.
Nonlinear behaviors of real-world physical systems are often abstracted into piecewise linear models by simplifying component parameters (parameter abstraction) or coarsening the time scale of behavior analysis (time scale abstraction). These two abstraction types correspond to two distinctly different discrete event iteration mechanisms that are active in between continuous modes. An important result of this research is an ontology of phase space transitions types in hybrid physical system models.
The research has developed a hybrid bond graph modeling paradigm that combines energy based bond graph models with finite state automata for discrete meta-level control of model configuration changes. This provides a systematic framework for behavior generation based on the physical principles of conservation of state and invariance of state. The principle of divergence of time verifies consistency of models using phase space analysis.
Current research is focused on the development of compositional modeling techniques and robust qualitative/quantitative simulators based on these principles, and the application of this approach for control, prediction, monitoring, and diagnosis of complex embedded processes.
In the area of Monitoring, Prediction and Fault Isolation, research has focused on the development of schemes for monitoring, prediction, and diagnosis of complex dynamic continuous systems. Earlier work applied diagnosis based on steady state models. Recent work has focused on monitoring and diagnosis from transient behaviors as faults occur in a system.
Diagnosis from dynamic transient behaviors is complex and requires close interaction of three modules: (i) monitoring, (ii) generation and refinement of fault hypotheses, and (iii) prediction. The research has established an architecture for model-based diagnosis of continuous systems that combines dynamic system models with the three integrated modules. Our initial modeling work starts from bond graphs and derives a temporal causal graph of dynamic system behavior. This underlying system model is used to identify system faults from deviating measurements and predict future behavior of the observed variables in terms of fault signatures which are expressed as parameter deviations and their magnitude and higher order derivatives. Behavior and diagnostic analysis is performed in a qualitative reasoning framework. The prediction module computes dynamic effects as a result of a fault in terms of qualitative magnitude changes and higher order derivative effects. A comparison of the monitored values with the predicted effects helps to refine initial candidate sets.
The current focus of the research is on studying a number of monitoring schemes that facilitate fault identification, progressive monitoring to facilitate discrepancy detection and refinement of fault candidates, and combining the use of qualitative and quantitative information to improve diagnostic accuracy. Future work will involve dealing with structural faults in the system, and multiple fault diagnosis.
With proliferation in embedded systems and their applications, this methodology takes on more and more significance. There are strong demands for real-time and online monitoring, fault isolation, and control of complex physical systems for safety reasons and performance issues. The computational burden imposed by real time online requirements necessitates the use of simplified models for analysis. As previously noted, this simplified models often produce lumped parameter representations that make it hard to establish cause effect relations between individual component parameters and observed transients in the system measurements. The present methodology provides a systematic approach to generating more informed predictions with simplified models that often result in the algebraic loops with negative feedback effects. The present application is directed to a solution for handling these negative feedback effects.